Method and apparatus for spectrum access of secondary users in cognitive radio system

ABSTRACT

A method and apparatus for spectrum access of secondary users in a cognitive radio system are provided. The method includes: estimating parameters of a primary channel according to the Markov model used by the primary channel; selecting a primary channel that has the largest available bandwidth according to the estimated channel parameters when multiple primary channels exist; detecting the selected primary channel; and accessing the primary channel to transmit data when the channel is idle. Therefore, an optimum primary channel can be selected from multiple primary channels by estimating channel parameters to satisfy high data transmission requirements and improve system performance.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to Chinese Patent Application No.200910001371.0, filed on Jan. 6, 2009, which is hereby incorporated byreference in its entirety.

FIELD OF THE INVENTION

The present invention relates to the network communication field, and inparticular, to a method and apparatus for spectrum access of secondaryusers in a cognitive radio system.

BACKGROUND OF THE INVENTION

In a radio communication system, radio spectrums are the most importantresources. Spectrums in existing networks are classified by the RadioRegulatory Commission according to different communication systems.Under such restrictions, many spectrum resources are not fully used.Communication rates of systems are restricted by their limited availablespectrum bandwidths. Therefore, the Cognitive Radio (CR) system isproposed in existing technologies to make better use of radio spectrumresources.

Users in a CR system are usually classified into primary users andsecondary users. A primary user owns radio spectrum resources. Asecondary user can use spectrum holes in the channels of a primary userto send or receive data in the case that the communication of theprimary user is not affected or that the impacts on the communicationare within a specified threshold.

In a CR system, common channel state models include a discrete-timeMarkov model and a continuous-time Markov model. FIG. 1 shows astructural diagram of the discrete-time Markov model. The statetransition probability of the discrete-time Markov model includes theprobabilities of the state changing from 0 to 1 and from 1 to 0, whichcan be represented by α and β. FIG. 2 shows the continuous-time Markovmodel. The idle time of a channel is represented by X (X₁, X₂ . . . ) asshown in FIG. 2. The busy time of a channel is represented by Y (Y₁, Y₂. . . ) as shown in FIG. 2. The idle time and busy time of a channel areexponentially distributed.

In the prior art, secondary users based on the Markov model need toestimate the channel usage of primary users and then select a channelaccording to the channel usage. That is, secondary users use thespectrum holes of channels of primary users to send or receive data.Take the discrete-time Markov model as an example. FIG. 3 shows aschematic diagram of the discrete-time Markov model and the channelusage of two primary users. For primary channel 1, time slots 2 and 4are spectrum holes. For primary channel 2, time slots 1, 4, and 5 arespectrum holes. In this case, secondary users can use the precedingspectrum holes to send or receive data.

In the preceding solution in the prior art, the parameters of the Markovmodel are supposed to be known and constant. In actual applicationscenarios of the CR, multiple primary channels may be available forselection. Secondary users, however, may not know the channel parametersof the models used by the primary channels. In this case, the prior artmay not select an optimum primary channel for access, thus affecting thesystem performance.

SUMMARY OF THE INVENTION

Embodiments of the present invention provide a method and apparatus forspectrum access of secondary users in a CR system. With the method andapparatus, an optimum primary channel may be selected from multipleprimary channels by estimating channel parameters to satisfy high datatransmission requirements and improve system performance.

A method for spectrum access of secondary users in a CR system includes:

estimating channel parameters of a primary channel according to theMarkov model used by the primary channel;

selecting a primary channel with the largest available bandwidthaccording to the estimated channel parameters when multiple primarychannels exist;

detecting the selected primary channel; and

accessing the primary channel to transmit data when the channel is idle.

An apparatus for spectrum access of secondary users in a CR systemincludes:

a parameter estimating unit, configured to estimate channel parametersof a primary channel according to the Markov model used by the primarychannel;

a channel selecting unit, configured to select a primary channel withthe largest available bandwidth according to the channel parametersestimated by the parameter estimating unit when multiple primarychannels exist;

a channel detecting unit, configured to detect the primary channelselected by the channel selecting unit; and

an accessing unit, configured to access the primary channel to transmitdata when the channel is idle.

A system for spectrum access of secondary users in a CR system includesa secondary user and a primary user.

The secondary user is configured to: estimate parameters of a primarychannel according to the Markov model used by the primary channel,select a primary channel that has the largest available bandwidthaccording to the estimated channel parameters when multiple primarychannels exist, detect the selected primary channel, and access theprimary channel to transmit data when the channel is idle.

The primary user is configured to transmit data with its radio spectrumresources.

It can be seen from the technical solutions of the embodiments of thepresent invention that a secondary user estimates the parameters of aprimary channel according to the Markov model used by the primarychannel, selects a primary channel that has the largest availablebandwidth according to the estimated channel parameters when multipleprimary channels exist, detects the selected primary channel, andaccesses the primary channel to transmit data when the channel is idle.Therefore, an optimum primary channel can be selected from multipleprimary channels by estimating channel parameters to satisfy high datatransmission requirements and improve system performance.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a structural diagram of the discrete-time Markov model inthe prior art;

FIG. 2 shows a structural diagram of the continuous-time Markov model inthe prior art;

FIG. 3 shows a schematic diagram of spectrum holes in the discrete-timeMarkov model in the prior art;

FIG. 4 shows a flowchart illustrating a method for spectrum access ofsecondary users according to a first embodiment of the presentinvention;

FIG. 5 a shows a structural diagram of an apparatus for spectrum accessof secondary users according to a second embodiment of the presentinvention;

FIG. 5 b shows a first schematic diagram of a parameter estimating unitof the apparatus according to the second embodiment of the presentinvention;

FIG. 5 c shows a second schematic diagram of a parameter estimating unitof the apparatus according to the second embodiment of the presentinvention;

FIG. 6 shows a structural diagram of a system according to a thirdembodiment of the present invention.

DETAILED DESCRIPTION OF THE EMBODIMENTS

The embodiments of the present invention provide a method and apparatusfor spectrum access of secondary users in a CR system, in which asecondary user selects an optimum primary channel from multiple primarychannels by estimating channel parameters to satisfy high datatransmission requirements and improve system performance.

FIG. 4 shows a flowchart illustrating a method for spectrum access ofsecondary users according to the first embodiment of the presentinvention. The method includes:

Step 41: estimating channel parameters of primary channels according tothe Markov model used by the primary channels. Further, the process forestimating the channel parameters of each primary channel includes:selecting an estimating method according to the Markov model used by theprimary channel, and estimating channel parameters of the primarychannel accordingly. In the embodiment of the present invention, theMarkov model used by the primary channel may be the discrete-time Markovmodel or continuous-time Markov model.

Taking the continuous-time Markov model as an example, the estimation ofchannel parameters includes the following process:

First, obtaining specific number of samples according to the estimationprecision required by the system and the standard normal distributionfunction, wherein the estimation precision required by the systemincludes relative estimation error and confidence probability. Therelative estimation error and confidence probability may be setaccording to the precision requirements of vendors. In the embodiment ofthe present invention, the specific number of samples is calculated asfollows:

$\begin{matrix}{r = {\frac{1}{\delta^{2}}\left\lbrack {\Phi^{- 1}\left( \frac{P_{C} + 1}{2} \right)} \right\rbrack}^{2}} & (1.1)\end{matrix}$In formula 1.1, δ is the relative estimation error; P_(C) is theconfidence probability; and φ(●) is the standard normal distributionfunction. For example, if δ is set to 1% and P_(C) is set to 99%, thenumber of samples can be calculated according to formula 1.1:r=25758.

Second, performing sampling according to the specific number of samplesr to obtain overall sampling result, and obtaining channel parameterλ_(X), λ_(Y) (unit: Hz) with the precision required by the systemaccording to the number of samples and overall sampling result. In theembodiment of the present invention, the channel parameters arecalculated as follows:

$\begin{matrix}{{\lambda_{X} = {\frac{r}{x_{1} + x_{2} + \ldots + x_{r}} = \frac{r}{\sum\limits_{k = 1}^{r}\; x_{k}}}}{\lambda_{Y} = {\frac{r}{y_{1} + y_{2} + \ldots + y_{r}} = \frac{r}{\sum\limits_{k = 1}^{r}\; y_{k}}}}} & (1.2)\end{matrix}$In formula 1.2, r is the total samples of X and Y, which may be thecalculated specific number of samples; X=(x₁, x₂, . . . ) and Y=(y₁, y₂,. . . ) are the overall sampling result, that is, the observationsequence.

If the primary channel uses the discrete-time Markov model, theestimation of channel parameters includes the following process:

Performing sampling according to number of initial sample. for example,in the embodiment of the present invention, the number of initial sampleis 100, and obtaining rough channel parameters according to the samplingresult. In the embodiment of the present invention, the rough channelparameters are calculated as follows:

$\begin{matrix}\left\{ \begin{matrix}{\alpha = {n_{1}/\left( {n_{0} + n_{1}} \right)}} \\{\beta = {n_{2}/\left( {n_{2} + n_{3}} \right)}}\end{matrix} \right. & (2.1)\end{matrix}$In formula 2.1, n₀, n₁, n₂, and n₃ respectively stand for the times offour kinds of one-step transition of the primary channel state, whereinthe kinds of the one-step transition include (0,0), (0,1), (1,0), (1,1).The total number of the four kinds of one-step transitions is theinitial number of samples. For example, according to formula 2.1, whenthe sampling result is n₁=2, n₀=38, n₂=5, and n₃=55, then the estimatedchannel parameters are: α=0.05, and β=0.08.

Obtaining specific number of samples required by the system according tothe rough channel parameters, the estimation precision required by thesystem, and the standard normal distribution function, where, estimationprecision required by the system includes relative estimation error andconfidence probability. The relative estimation error and confidenceprobability may be set according to the precision requirements ofvendors. In the implementation, the specific number of samples may becalculated as follows:

$\begin{matrix}{{r_{\alpha} = {\frac{\left\lbrack {\Phi^{- 1}\left( \frac{P_{C}^{\alpha} + 1}{2} \right)} \right\rbrack^{2}}{\delta_{\alpha}^{2}}\left( {1 - \alpha} \right)\left( {\frac{1}{\alpha} + \frac{1}{\beta}} \right)}}{r_{\beta} = {\frac{\left\lbrack {\Phi^{- 1}\left( \frac{P_{C}^{\beta} + 1}{2} \right)} \right\rbrack^{2}}{\delta_{\beta}^{2}}\left( {1 - \beta} \right)\left( {\frac{1}{\alpha} + \frac{1}{\beta}} \right)}}{r_{1} = {\max\left( {r_{\alpha},r_{\beta}} \right)}}} & (2.2)\end{matrix}$In formula 2.2, δ_(α) and δ_(β) are relative estimation errors; P_(C)^(α) and P_(C) ^(β) are confidence probabilities; φ(●) is the standardnormal distribution function; r_(α) and r_(β) are the specific number ofsamples required by rough channel parameters α and β under correspondingestimation precision; r₁ is the specific number of samples required bythe system under corresponding estimation precision. For example, ifδ_(α) is set to 0.05, P_(C) ^(α) is set to 99%, and α is 0.05 and β is0.08 according to the preceding estimation, then the number of samplesmay be obtained:r_(α)=31811

Similarly, if δ_(β) is set to 0.05, P_(C) ^(β) is set to 99%, and α is0.05 and β is 0.08 according to the preceding estimation, the number ofsamples is:r_(β)=30807

Performing left sampling according to the specific number of samplesrequired by the system, and the number of initial sample which has beenperformed, and further, obtaining overall sampling result, wherein thenumber of the left sampling is obtained by deducting the initial numberof samples from the specific number of samples required by the system.

Obtaining the channel parameter with the precision required by thesystem according to the specific number of samples and overall samplingresult. In the embodiment, the calculation of the channel parameters isalso performed according to formula 1.2. For example, if the specificnumber of samples r_(α) is 31811 and r_(β) is 30807, and sampling hasbeen performed for 100 times, 31711 more samples should be collected forthe channel parameter α to satisfy the estimation precision required bythe system.

Step 42: selecting a primary channel with the largest availablebandwidth according to the estimated channel parameters when multipleprimary channels exist.

If multiple primary channels need to be estimated, the process ofchannel selection is added. Specifically, the estimated channelparameters are used to select a primary channel with the largestavailable bandwidth.

In the embodiment of the present invention, if these primary channelsuse the continuous-time Markov model, a primary channel with the largestavailable bandwidth may be selected according to the bandwidths andchannel parameters of these primary channels, the primary channel withthe largest available bandwidth is confirmed according to the formula3.1:

$\begin{matrix}{i_{*} = {\arg\;{\max\limits_{{i = 1},\ldots\mspace{14mu},N}{\frac{\frac{1}{\lambda_{yi}}}{\left( {\frac{1}{\lambda_{xi}} + \frac{1}{\lambda_{yi}}} \right)}B_{i}}}}} & (3.1)\end{matrix}$

In formula 3.1, B_(i) is the bandwidth of the primary channel i; λ_(xi)and λ_(yi) are channel parameters of the primary channel i. For example,if five primary channels are available for selection, after the channelparameters of the five primary channels are estimated, bandwidths of thefive primary channels may be calculated accordingly. The primary channelwith the largest available bandwidth is the selected primary channel.

If the primary channels use the discrete-time Markov model, the primarychannel with the largest available bandwidth is selected according tothe bandwidths of the primary channels, the probability that a secondaryuser uses a primary channel, and the channel parameters of these primarychannels. In the embodiment, the primary channel with the largestavailable bandwidth is confirmed according to the formula 3.2:

$\begin{matrix}{i_{*} = {\arg\;{\max\limits_{{i = 1},\ldots\mspace{14mu},N}{\left( {{\mu_{i}\beta_{i}} + {\left( {1 - \mu_{i}} \right)\alpha_{i}}} \right)B_{i}}}}} & (3.2)\end{matrix}$

In formula 3.2, B_(i) is the bandwidth of the primary channel i; μ_(i)is the probability that a secondary user uses the primary channel i;α_(i) and β_(i) are channel parameters of the primary channel i.Similarly, if multiple primary channels are available for selection,after the channel parameters α,β of the multiple primary channels areestimated, bandwidths of the primary channels may be calculatedaccording to the formula. The primary channel with the largest availablebandwidth is the selected primary channel. In addition, when formula 3.2is used:

${\mu_{i}(t)} = \left\{ \begin{matrix}1 & {{{{if}\mspace{14mu}{a(t)}} = i},{{\Theta_{a}(t)} = 1}} \\0 & {{{{if}\mspace{14mu}{a(t)}} = i},{{\Theta_{a}(t)} = 0}} \\{{{u_{i}\left( {t - 1} \right)}\beta_{i}} + {\left( {1 - {\mu_{i}\left( {t - 1} \right)}} \right)\alpha_{i}}} & {{{if}\mspace{14mu}{a(t)}} \neq i}\end{matrix} \right.$In the preceding formula, a(t) is the channel index observed by thesystem at timeslot t; Θ_(a)(t) is the observation result of channel attimeslot t. When the channel is busy, the value of Θ_(a)(t) is 1; whenthe channel is idle, the value is 0. Meanings of other parameters arethe same as those in formula 3.2. The preceding formula periodicallymodifies the states of channels at each timeslots according tohistorical and current observation results.

Step 43: detecting the selected primary channel.

After estimating the parameters of the primary channel, detect theprimary channel to judge whether the channel is idle.

Step 44: transmitting data through the primary channel if the channel isidle.

The secondary user starts the detection of the primary channel. If thechannel is occupied, the secondary user continues the detection. If thechannel is idle, the secondary user uses the idle channel to transmitdata.

If the primary channel uses the continuous-time Markov model, theduration of data transmission is obtained according to the maximumthreshold of the conflict probability produced by the use of the primarychannel by the secondary user that is constrained by the set spectrumetiquette, the set correction factor, the set detection duration, andthe estimated channel parameters. In the embodiment, the duration ofdata transmission is calculated as follows:

$\begin{matrix}{T_{P} = {\min\left( {\frac{\sqrt{T_{D}^{2} + \frac{4T_{D}}{\lambda_{X}}} - T_{D}}{2},{\gamma_{P} \cdot \left\lbrack {\frac{1}{\lambda_{X}}{\ln\left( \frac{1}{1 - \eta} \right)}} \right\rbrack}} \right)}} & (1.3)\end{matrix}$In formula 1.3, η is the maximum threshold of the conflict probabilitycoursed by the use of the primary channel by the secondary user that isconstrained by the preset spectrum etiquette; γ_(P) is the correctionfactor and complies with the condition of γ_(P)∈(0,1). The correctionfactor may be set according to the estimation error and channel burst toprovide protection of 1/γ_(P)−1 for the relative estimation error. Itmay also suppress the abrupt increase of the conflict probability causedby the abrupt change of channel parameters. T_(D) is the detectionduration; λ_(X) is the estimated channel parameters. For example, basedon formula 1.2, λ_(X) is 1 Hz. If η is set to 0.2, γ_(P) is set to 0.9,and T_(D) is 2 s, then according to formula 1.3, T_(P) is 0.2008 s.

After the secondary user transmits data through the idle channel, thechannel may be detected again for the next data transmission.

In addition, if the primary channel uses the discrete-time Markov model,the duration of the data transmission is the timeslot length of thediscrete-time Markov model.

Through the implementation of the preceding technical solution, asecondary user obtains the spectrum holes of a primary channel when thechannel parameters of the primary channel model are unknown. Whenmultiple primary channels are available, an optimum primary channel maybe selected from multiple primary channels by estimating channelparameters to satisfy high data transmission requirements and improvesystem performance. The secondary user detects only the optimum primarychannel. Therefore, the detection workload is reduced.

In addition, if the primary channel uses the continuous-time Markovmodel, after the estimation of the channel parameters of the primarychannel, the secondary user monitors the probability P_(R) of the latestconflicts when using the primary channel. If the probability P_(R)exceeds preset characteristic value, the channel parameters of thecontinuous-time Markov model are marked as unavailable and the channelparameters are re-estimated.

The set characteristic value may be obtained according to the estimatedchannel parameters and the data transmission duration. In the embodimentof the present invention, the characteristic value is set in a range of[aP₁,bP₁], in which a is greater than 0 and smaller than 1 and b isgreater than 1. The parameter P₁ may be obtained according to theformula P_(I)=P(X≦T_(P))=1−e^(−λ) ^(X) ^(T) ^(P) ≦η. In the formula,meanings of T_(P), η and λ_(X) are the same as those in formula 1.3. Forexample, according to formula 1.2, λ_(X) is 1 Hz. According to formula1.3, T_(P) is 0.2008 s. If η is 0.2, then according to the precedingformula, P_(I) is equal to 0.1819. Therefore, the range of thecharacteristic value is obtained.

The estimation of channel parameters may be restarted by monitoring thechannel model when the channel parameters change constantly. Therefore,spectrum holes of the primary channel may be obtained for datatransmission, thus improving system performance.

The second embodiment of the present invention provides an apparatus forspectrum access of secondary users in the CR system. FIG. 5 a shows astructural diagram of the apparatus. The apparatus includes a parameterestimating unit 51, a channel selecting unit 52, a channel detectingunit 53, and an accessing unit 54.

The parameter estimating unit 51 is configured to estimate the channelparameters of a primary channel according to the Markov model used bythe primary channel. For details on the method for estimating channelparameters, see the description of the first embodiment of the presentinvention.

The channel selecting unit 52 is configured to select a primary channelwith the largest available bandwidth according to the channel parametersestimated by the parameter estimating unit 51 when multiple primarychannels exist.

The channel detecting unit 53 is configured to detect the primarychannel selected by the channel selecting unit 52.

The accessing unit 54 is configured to access the idle channel detectedby the channel detecting unit 53 to transmit data.

As shown in FIG. 5 b, the parameter estimating unit 51 may furtherinclude:

a first sample number obtaining unit 511, configured to obtain thespecific sample number according to the relative estimation errorrequired by the system, confidence probability, and standard normaldistribution function, when the continuous-time Markov model is used bythe primary channel;

a first sampling unit 512, configured to: perform sampling according tothe obtained sample number and obtain the overall sampling result; and

a first channel parameter obtaining unit 513, configured to obtainchannel parameters required by the system according to the specificsample number and overall sampling result.

Or, as shown in FIG. 5 c, the parameter estimating unit 51 may furtherinclude:

a second sampling unit 515, configured to obtain the initial samplingresult by sampling according to the preset initial sample number andobtain sampling result by sampling according to the result of thespecific sample number minus the initial sample number;

a second channel parameter obtaining unit 516, configured to: obtainrough channel parameters according to the number of transitions ofchannel states under the sampling result obtained according to theinitial sample number, or obtain the channel parameters required by thesystem according to the number of transitions of channel states underthe overall sampling result obtained according to the result of thespecific sample number minus the initial sample number and deliver thechannel parameters to other units;

a second specific sample number obtaining unit 517, configured to obtainthe specific sample number according to the rough channel parameters,relative estimation error, confidence probability, and standard normaldistribution function.

In the embodiment of the present invention, the apparatus furtherincludes: a transmission duration obtaining unit 55, configured toobtain the data transmission duration according to the maximum thresholdof the conflict probability produced by the use of the primary channelby the secondary user that is constrained by the set spectrum etiquette,the set correction factor, the set detection duration, and the channelparameters estimated by the parameter estimating unit 51 when theprimary channel uses the continuous-time Markov model.

The apparatus may further include: a parameter re-estimating unit 56,configured to monitor the probability of the latest conflicts after theidle primary channel is accessed to transmit data when the primarychannel uses the continuous-time Markov model, in which if theprobability exceeds the set characteristic value, the channel parametersof the continuous-time Markov model are marked as unavailable and thechannel parameters are re-estimated. The parameter re-estimating unit 52and access unit 54 are parallel units. That is, after detecting thechannel, the channel detecting unit 53 simultaneously triggers theparameter re-estimating unit 56 and accessing unit 54. For details onthe method for re-estimating channel parameters, see the description ofthe first embodiment of the present invention.

The third embodiment of the present invention provides a system forspectrum access of secondary users in the CR system. FIG. 6 shows astructural diagram of the system. The system includes a secondary user61 and a primary user 62.

The secondary user 61 is configured to: estimate the parameters of aprimary channel according to the Markov model used by the primarychannel, select a primary channel that has the largest availablebandwidth according to the estimated channel parameters when multipleprimary channels exist, detect the selected primary channel, and accessthe primary channel to transmit data when the channel is idle.

The primary user 62 is configured to transmit data with its radiospectrum resources.

The secondary user 61 further includes a transmission duration obtainingunit 611, which is configured to obtain the data transmission durationaccording to the estimated channel parameters, the maximum threshold ofthe conflict probability produced by the use of the primary channel bythe secondary user that is constrained by the set spectrum etiquette,preset correction factor, and preset detection duration when the primarychannel uses the continuous-time Markov model.

In addition, the secondary user 61 may serve as the spectrum accessapparatus in the second embodiment of the present invention, includingunits of the spectrum access apparatus.

It should be noted that in the embodiments of the present invention, theunits of the apparatus and system are divided logically and functionallyand are not limited to the preceding division. Units that implementcorresponding functions can be used herein. In addition, names of unitsare only for distinguishing and are not used to limit the scope of thepresent invention.

It is understandable to those skilled in the art that all or part of thesteps in the method may be performed through hardware instructed by aprogram. The program may be stored in a computer-readable storage mediumsuch as a read-only memory, a magnetic disk, and a compact disk.

To sum up, according to the embodiments of the present invention, anoptimum primary may be selected from multiple primary channels byestimating channel parameters to satisfy high data transmissionrequirements and improve system performance.

Although the invention has been described through several exemplaryembodiments, the invention is not limited to such embodiments. It isapparent that those skilled in the art can make various modificationsand variations to the invention without departing from the spirit andscope of the invention. The invention is intended to cover themodifications and variations provided that they fall in the scope ofprotection defined by the following claims or their equivalents.

What is claimed is:
 1. A method for spectrum access, comprising:estimating channel parameters of primary channels according to theMarkov model used by the primary channels; selecting a primary channelwith a largest available bandwidth according to the estimated channelparameters; and detecting the selected primary channel, and transmittingdata through the selected primary channel if the primary channel isidle; wherein if the Markov model used b the primary channel isdiscrete-time Markov model, the step of estimating the channelparameters of the primary channels according to the Markov model used bthe primary channel comprises: performing sampling according to a numberof an initial sample and obtaining rough channel parameters according toan initial sampling result; obtaining a specific number of samplesrequired by the system according to the rough channel parametersestimation precision required by the system and standard normaldistribution function; performing left sampling according to thespecific number of samples required by the system, and the number of theinitial sample which has been performed; obtaining an overall samplingresult; and obtaining a channel parameter with the estimation precisionrequired by the system according to the specific number of samples andthe overall sampling result; wherein if the primary channels use acontinuous-time Markov model, the primary channel with the largestavailable bandwidth is confirmed according to a formula of${i_{*} = {\arg\;{\max\limits_{{i = 1},\ldots\mspace{14mu},N}\;{\frac{\frac{1}{\lambda_{yi}}}{\left( {\frac{1}{\lambda_{xi}} + \frac{1}{\lambda_{yi}}} \right)}B_{i}}}}},$wherein, B_(i) represents the bandwidth of the primary channel I, andλ_(xi) and λ_(yi) represents channel parameters of the primary channelI; and if the primary channels use a discrete-time Markov model, theprimary channel with the largest available bandwidth is selectedaccording to a formula of$i_{*} = {\arg\;{\max\limits_{{i = 1},\ldots\mspace{14mu},N}{\left( {{\mu_{i}\beta_{i}} + {\left( {1 - \mu_{i}} \right)\alpha_{i}}} \right)B_{i}}}}$wherein B_(i), represents the bandwidth of the primary channel i, μ_(i)represents probability that a secondary user uses the primary channeland i; α_(i) and β_(i) represents channel parameters of the primarychannel i.
 2. The method according to claim 1, wherein if the Markovmodel used by the primary channel is continuous-time Markov model, thestep of estimating the channel parameters of the primary channelsaccording to the Markov model used by the primary channel comprises:obtaining the specific number of samples according to the estimationprecision required by the system and the standard normal distributionfunction; performing the sampling according to the specific number ofsamples, and obtaining the overall sampling result; and obtaining thechannel parameter with the precision required by the system according tothe specific number of samples and the overall sampling result.
 3. Themethod according to claim 2, wherein the estimation precision requiredby the system comprises relative estimation error and confidenceprobability.
 4. The method according to claim 2, wherein the specificnumber of samples is calculated according to a formula of$r = {\frac{1}{\delta^{2}}\left\lbrack {\Phi^{- 1}\left( \frac{P_{C} + 1}{2} \right)} \right\rbrack}^{2}$wherein, r represents the specific number of samples, P_(c) representsconfidence probability, Φ(•) represents standard normal distributionfunction.
 5. The method according to claim 2, wherein the channelparameters are calculated according to formulas of$\lambda_{X} = {\frac{r}{x_{1} + x_{2} + \ldots + x_{r}} = \frac{r}{\sum\limits_{k = 1}^{r}\; x_{k}}}$$\lambda_{Y} = {\frac{r}{y_{1} + y_{2} + \ldots + y_{r}} = \frac{r}{\sum\limits_{k = 1}^{r}\; y_{k}}}$wherein, r represents the specific number of samples, X=(x₁,x₂, . . . )and Y=(y₁y₂, . . . ) are the overall sampling result, that is, theobservation sequence.
 6. The method according to claim 1, wherein therough channel parameters are obtained according to a formula of$\left\{ \begin{matrix}{\alpha = {n_{1}/\left( {n_{0} + n_{1}} \right)}} \\{\beta = {n_{2}/\left( {n_{2} + n_{3}} \right)}}\end{matrix} \right.$ wherein, n₀, n₁, n₂, and n₃ respectively representfor times of four kinds of one-step transition of the primary channelstate.
 7. The method according to claim 1, wherein the estimationprecision required by the system comprises relative estimation error andconfidence probability.
 8. The method according to claim 1, wherein thespecific number of samples is obtained according to formulas of$r_{\alpha} = {\frac{\left\lbrack {\Phi^{- 1}\left( \frac{P_{C}^{\alpha} + 1}{2} \right)} \right\rbrack^{2}}{\delta_{\alpha}^{2}}\left( {1 - \alpha} \right)\left( {\frac{1}{\alpha} + \frac{1}{\beta}} \right)}$$r_{\beta} = {\frac{\left\lbrack {\Phi^{- 1}\left( \frac{P_{C}^{\beta} + 1}{2} \right)} \right\rbrack^{2}}{\delta_{\beta}^{2}}\left( {1 - \beta} \right)\left( {\frac{1}{\alpha} + \frac{1}{\beta}} \right)}$r = max (r_(α), r_(β)) wherein, δ_(α) and δ_(β) represents theestimation errors, P_(c) ^(α) and P_(c) ^(β) represents confidenceprobabilities, Φ(•) represents the standard normal distributionfunction, r_(α) and r_(β) respectively represents the specific number ofsamples required by parameters α and β under corresponding estimationprecision, r represents the specific number of samples required by thesystem under corresponding estimation precision.
 9. The method accordingto claim 1, wherein the channel parameters are calculated according toformulas of$\lambda_{X} = {\frac{r}{x_{1} + x_{2} + \ldots + x_{r}} = \frac{r}{\sum\limits_{k = 1}^{r}\; x_{k}}}$$\lambda_{Y} = {\frac{r}{y_{1} + y_{2} + \ldots + y_{r}} = \frac{r}{\sum\limits_{k = 1}^{r}\; y_{k}}}$wherein, r represents the specific number of samples, X=(x₁, x₂, . . . )and Y=(y₁,y₂, . . . ) are the overall sampling result, that is, theobservation sequence.
 10. The method according to claim 1, wherein theprobability μ_(i), that a secondary user uses the primary channel i iscalculated according to a formula of${\mu_{i}(t)} = \left\{ \begin{matrix}1 & {{{{if}\mspace{14mu}{a(t)}} = i},{{\Theta_{a}(t)} = 1}} \\0 & {{{{if}\mspace{14mu}{a(t)}} = i},{{\Theta_{a}(t)} = 0}} \\{{{u_{i}\left( {t - 1} \right)}\beta_{i}} + {\left( {1 - {\mu_{i}\left( {t - 1} \right)}} \right)\alpha_{i}}} & {{{if}\mspace{14mu}{a(t)}} \neq i}\end{matrix} \right.$ wherein, a(t) represents channel index observed bythe system at timeslot t, Θ_(a)(t) is the observation result of channelat timeslot t.
 11. The method according to claim 1, wherein if theprimary channel with the largest available bandwidth uses thecontinuous-time Markov model, the duration of data transmission isobtained according to a formula of$T_{P} = {\min\left( {\frac{\sqrt{T_{D}^{2} + \frac{4T_{D}}{\lambda_{X}}} - T_{D}}{2},{\gamma_{P} \cdot \left\lbrack {\frac{1}{\lambda_{X}}{\ln\left( \frac{1}{1 - \eta} \right)}} \right\rbrack}} \right)}$wherein, η represents maximum threshold of the conflict probabilitycoursed by use of the primary channel by the secondary user that isconstrained by preset spectrum etiquette; γ_(P) represents correctionfactor and complies with the condition of γ_(P)ε(0,1), γ_(P) is setaccording to estimation error and channel burst to provide protection of1/γ_(P)−1 for the relative estimation error, T_(D) represents detectionduration; λ_(X) represents estimated channel parameter; if the primarychannel uses the discrete-time Markov model, the duration of the datatransmission is the timeslot length of the discrete-time Markov model.12. The method according to claim 1, wherein if the primary channel usesthe continuous-time Markov model, the method further comprises:monitoring probability of the latest conflicts, if the probabilityexceeds a preset characteristic value, marking the channel parameters ofthe primary channel as unavailable, and re-estimating the channelparameters.
 13. The method according to claim 12, wherein thecharacteristic value is set in a range of [aP₁, bP₁],wherein a isgreater than 0 and smaller than 1 and b is greater than 1, and theparameter P₁ is obtained according to the formulaP _(I) =P(X≦T _(P))=1−e ^(−λ) ^(X) ^(T) ^(P) ≦η, the parameter T_(p)represents duration of data transmission and is obtained according to aformula of$T_{P} = {\min\left( {\frac{\sqrt{T_{D}^{2} + \frac{4T_{D}}{\lambda_{X}}} - T_{D}}{2},{\gamma_{P} \cdot \left\lbrack {\frac{1}{\lambda_{X}}{\ln\left( \frac{1}{1 - \eta} \right)}} \right\rbrack}} \right)}$wherein, η represents maximum threshold of the conflict probabilitycoursed by use of the primary channel by the secondary user that isconstrained by preset spectrum etiquette; γ_(P) represents correctionfactor and complies with the condition of γ_(P)ε(0,1), γ_(P) is setaccording to estimation error and channel burst to provide protection of1/γ_(P)−1 for the relative estimation error, T_(D) represents detectionduration; λ_(X) represents estimated channel parameter; if the primarychannel uses the discrete-time Markov model, the duration of the datatransmission is the timeslot length of the discrete-time Markov model.14. An apparatus for spectrum access, comprising: a parameter estimatingunit, configured to estimate channel parameters of primary channelsaccording to Markov model used by the primary channel; a channelselecting unit, configured to select a primary channel with the largestavailable bandwidth according to the channel parameters; an accessingunit, configured to access the primary channel with the largestavailable bandwidth; and a channel detecting unit, configured to detectthe primary channel selected by the channel selecting unit, and theaccessing unit only accesses idle primary channel detected by thechannel detecting unit; wherein if the Markov model used b the primarychannel is discrete-time Markov model, the step of estimating thechannel parameters of the primary channels according to the Markov modelused by the primary channel comprises: performing sampling according toa number of an initial sample, and obtaining rough channel parametersaccording to an initial sampling result; obtaining a specific number ofsamples required by the system according to the rough channelparameters, estimation precision required by the system, and standardnormal distribution function; performing left sampling according to thespecific number of samples required by the system, and the number of theinitial sample which has been performed; obtaining an overall samplingresult; and obtaining a channel parameter with the estimation precisionrequired by the system according to the specific number of samples andthe overall sampling result; wherein if the primary channels use acontinuous-time Markov model, the primary channel with the largestavailable bandwidth is confirmed according to a formula of${i_{*} = {\arg\;{\max\limits_{{i = 1},\ldots\mspace{14mu},N}{\frac{\frac{1}{\lambda_{yi}}}{\left( {\frac{1}{\lambda_{xi}} + \frac{1}{\lambda_{yi}}} \right)}B_{i}}}}},$wherein, B_(i), represents the bandwidth of the primary channel i, andλ_(xi) and λ_(yi) represents channel parameters of the primary channeli; and if the primary channels use a discrete-time Markov model, theprimary channel with the largest available bandwidth is selectedaccording to a formula of$i_{*} = {\arg\;{\max\limits_{{i = 1},\ldots\mspace{14mu},N}{\left( {{\mu_{i}\beta_{i}} + {\left( {1 - \mu_{i}} \right)\alpha_{i}}} \right)B_{i}}}}$wherein B_(i) represents the bandwidth of the primary channel i,μ_(i)represents probability that a secondary user uses the primarychannel i; α_(i) and β_(i) represents channel parameters of the primarychannel i.
 15. The method according to claim 14, wherein ifcontinuous-time Markov model is used by the primary channels, theparameter estimating unit comprises: a first sample number obtainingunit, configured to obtain specific sample number according to relativeestimation required by the system, confidence probability, and standardnormal distribution function; a first sampling unit, configured toperform sampling according to the specific sample number and overallsampling result accordingly; a first channel parameter obtaining unit,configured to obtain channel parameters required by the system accordingto the specific sample number and overall sampling result.
 16. Theapparatus according to claim 14, wherein if discrete-time Markov modelis used by the primary channel, the parameter estimating unit comprises:a second sampling unit, configured to obtain initial sampling resultaccording to preset initial sample number, and obtain sampling resultaccording to result of the specific sample number minus the initialsample number; a second channel parameter obtaining unit, configured toobtain rough channel parameters according to the initial samplingresult, and obtain channel parameters required system according tonumber of transitions of channel states under the overall samplingresult according to the result of the specific sample number minus theinitial sample number; a second specific sample number obtaining unit,configured to obtain the specific sample number according to the roughchannel parameters, relative estimation error, confidence probability,and standard normal distribution function.
 17. A system for spectrumaccess of secondary users in a Cognitive Radio (CR) system, wherein thesystem comprises: a secondary user, configured to estimate parameters ofprimary channels according to Markov model used by the primary channel,select a primary channel with the largest available bandwidth accordingto the estimated channel parameters, and access the primary channel totransmit data when the channel is idle; and a primary user, configuredto transmit data with its radio spectrum resources; wherein if theMarkov model used by the primary channel is discrete-time Markov model,the step of estimating the channel parameters of the primary channelsaccording to the Markov model used by the primary channel comprises:performing sampling according to a number of an initial sample, andobtaining rough channel parameters according to an initial samplingresult; obtaining a specific number of samples required by the systemaccording to the rough channel parameters, estimation precision requiredby the system, and standard normal distribution function; performingleft sampling according to the specific number of samples required bythe system, and the number of the initial sample which has beenperformed; obtaining an overall sampling result; and obtaining a channelparameter with the estimation precision required by the system accordingto the specific number of samples and the overall sampling result;wherein if the primary channels use a continuous-time Markov model, theprimary channel with the largest available bandwidth is confirmedaccording to a formula of${i_{*} = {\arg\;{\max\limits_{{i = 1},\ldots\mspace{14mu},N}\;{\frac{\frac{1}{\lambda_{yi}}}{\left( {\frac{1}{\lambda_{xi}} + \frac{1}{\lambda_{yi}}} \right)}B_{i}}}}},$wherein, B_(i) represents the bandwidth of the primary channel i, andλ_(xi) and λ_(yi) represents channel parameters of the primary channeli; and if the primary channels use a discrete-time Markov model, theprimary channel with the largest available bandwidth is selectedaccording to a formula of$i_{*} = {\arg\;{\max\limits_{{i = 1},\ldots\mspace{14mu},N}{\left( {{\mu_{i}\beta_{i}} + {\left( {1 - \mu_{i}} \right)\alpha_{i}}} \right)B_{i}}}}$wherein, B_(i) represents the bandwidth of the primary channel i; μ_(i)represents probability that a secondary primary channel i; α_(i) andβ_(i) represents channel parameters of the primary channel i.